import os
import sys
import random

import shutil
from tensorflow.python.lib.io.file_io import FileIO

import cv2
import numpy as np
import tensorflow as tf
from PIL import Image
from matplotlib import pyplot as plt
from object_detection.utils import label_map_util, visualization_utils

sys.path.append("..")

# What machine-learning-training to download.
MODEL_NAME = 'ssd_mobilenet_v1_coco_11_06_2017'
MODEL_FILE = MODEL_NAME + '.tar.gz'
DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/'

# Path to frozen detection graph. This is the actual machine-learning-training that is used for the object detection.
# PATH_TO_CKPT = 'frozen_inference_graph_leo_with_or_without_helmet.pb'
PATH_TO_CKPT = 'D:/work/workspace/ml_test_data/gjdw_35999.pb'

# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = os.path.join('data', 'mscoco_label_map.pbtxt')

NUM_CLASSES = 90

# opener = urllib.request.URLopener()
# opener.retrieve(DOWNLOAD_BASE + MODEL_FILE, MODEL_FILE)

detection_graph = tf.Graph()
with detection_graph.as_default():
    od_graph_def = tf.GraphDef()
    with FileIO(PATH_TO_CKPT, 'rb') as fid:
        serialized_graph = fid.read()
        od_graph_def.ParseFromString(serialized_graph)
        tf.import_graph_def(od_graph_def, name='')
        label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
        categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES,
                                                                          use_display_name=True)
        category_index = label_map_util.create_category_index(categories)


def load_image_into_numpy_array(image):
    (im_width, im_height) = image.size
    return convert_image_data_to_numpy_array(image.getdata(), im_height, im_width)


def convert_image_data_to_numpy_array(data, im_height, im_width):
    return np.array(data).reshape(
        (im_height, im_width, 3)).astype(np.uint8)


def convert_image_array_to_image(image_array, image_name):
    new_image = Image.fromarray(image_array, "RGB")
    new_image.save(image_name)


# For the sake of simplicity we will use only 2 images:
# image1.jpg
# image2.jpg
# If you want to test the code with your images, just add path to the images to the TEST_IMAGE_PATHS.
PATH_TO_TEST_IMAGES_DIR = 'test_images'

# Size, in inches, of the output images.
IMAGE_SIZE = (12, 8)

with detection_graph.as_default():
    with tf.Session(graph=detection_graph) as sess:
        # Definite input and output Tensors for detection_graph
        image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
        # Each box represents a part of the image where a particular object was detected.
        detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
        # Each score represent how level of confidence for each of the objects.
        # Score is shown on the result image, together with the class label.
        detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
        detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
        num_detections = detection_graph.get_tensor_by_name('num_detections:0')
        cv2.startWindowThread()
        for root, image_path, dir in os.walk('test_images'):
            for file in dir:
                image = Image.open('test_images/' + file)
                # the array based representation of the image will be used later in order to prepare the
                # result image with boxes and labels on it.
                image_np = load_image_into_numpy_array(image)
                # Expand dimensions since the machine-learning-training expects images to have shape: [1, None, None, 3]
                image_np_expanded = np.expand_dims(image_np, axis=0)
                # Actual detection.
                (boxes, scores, classes, num) = sess.run(
                    [detection_boxes, detection_scores, detection_classes, num_detections],
                    feed_dict={image_tensor: image_np_expanded})

                # Visualization of the results of a detection.
                box_to_color_map = visualization_utils.visualize_boxes_and_labels_on_image_array(
                    image_np,
                    np.squeeze(boxes),
                    np.squeeze(classes).astype(np.int32),
                    np.squeeze(scores),
                    category_index,
                    use_normalized_coordinates=True,
                    line_thickness=8)
                plt.figure(figsize=IMAGE_SIZE)
                plt.imshow(image_np)
                print(box_to_color_map)
                cv2.imshow('result', image_np)

                result = Image.fromarray(image_np)
                result_file_name = str(int(random.random() * 100000)) + file
                FileIO(result_file_name, 'w')
                result.save(result_file_name, 'jpeg')
                shutil.move(result_file_name, './result/' + result_file_name)

                if cv2.waitKey(1) & 0xFF == ord('q'):
                    break
             # convert_image_array_to_image(image_np, image_path)
